Summary of A Unified Framework For Forward and Inverse Problems in Subsurface Imaging Using Latent Space Translations, by Naveen Gupta et al.
A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations
by Naveen Gupta, Medha Sawhney, Arka Daw, Youzuo Lin, Anuj Karpatne
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Mathematical Physics (math-ph); Geophysics (physics.geo-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a unified framework called the Generalized Forward-Inverse (GFI) framework to characterize prior research in deep learning for subsurface imaging. The authors aim to address open questions in this area, including the impact of latent space sizes, manifold learning, and translation models. They develop two new model architectures, Latent U-Net and Invertible X-Net, which leverage the strengths of U-Nets and IU-Nets for domain translation and simultaneous forward-inverse translations. The proposed models achieve state-of-the-art performance on synthetic datasets and demonstrate zero-shot effectiveness on real-world-like datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to use deep learning to create images of what’s underneath the ground. It wants to answer some big questions about how this works, like how important it is to learn both the forward (what’s underneath) and inverse (how we get that information) problems together. To do this, the researchers propose a new framework called GFI, which combines ideas from previous work in this area. They also come up with two new models, Latent U-Net and Invertible X-Net, to help make these images better. The results show that their methods are really good at creating these images and can even do it without needing any more training data. |
Keywords
» Artificial intelligence » Deep learning » Latent space » Manifold learning » Translation » Zero shot
